基于广义Udwadia-Kalaba的无人机约束跟踪控制

无人机技术的迅速进步推动了其在智能控制系统中广泛应用,尤其在探索、运输、监视等领域。对于旋翼式无人机的自主操作,安全性至关重要,因此控制方法必须遵守约束条件,否则可能导致严重后果。约束存在于机械系统中,包括不等式约束和伺服等式约束,不合理处理这些约束可能影响系统的控制精度和稳定性。为了确保无人机在恶劣环境中表现良好,本文提出了一种基于广义Udwadia-Kalaba方程的控制算法,解决了不等式约束和非线性系统特性下的约束跟踪控制问题,确保系统的稳定性和性能。

一种新型电动汽车换挡位移约束跟踪控制

针对同步器位移约束跟踪控制问题,基于一款新型电动汽车集成电驱系统,提出了一种新颖的控制方法。对该电驱系统换挡机构及换挡过程进行了动力学建模;将同步器结合套的结合规律看作一种位移约束,利用Udwadia-Kalaba方法在不引入Lagrange乘子等参数的情况下对约束力进行了求解;利用Udwadia-Kalaba方法与PID方法的加权设计了一种同步器换挡位移跟踪控制器;通过Matlab设计进行了多组仿真,并完成了实车验证试验。仿真与试验结果显示:在该控制器作用下,同步器结合轨迹能够较好地完成目标轨迹跟踪;与传统PID方法相比,新控制器具有较强的跟踪控制动态特性、较高的稳态精度和较小的冲击度,累计跟踪误差仿真结果减少25%以上,位移约束跟踪误差试验结果在4%以内。并且针对不同换挡目标约束可以有效完成目标轨迹跟踪,从而有效地提高换挡品质。

基于虚位移分解与伺服轨迹约束的机械系统跟踪控制

建立在Udwadia和Kalaba方程上的伺服约束跟踪控制方法要求系统的初始条件必须与跟踪约束方程相容。针对该问题,利用广义虚位移投影分解方法,将跟踪约束误差转化为广义坐标误差与广义速度误差,通过提出系统跟踪控制总误差的概念,提出一种可以消除该误差的控制力计算方法。该方法可以实现对系统初始误差的补偿。利用该算法对系统初始跟踪误差的可控性,将其与基于Udwadia和Kalaba方程的伺服约束跟踪控制算法结合,实现了不相容初始条件下的约束系统轨迹跟踪控制。以平面2自由度机械手臂为仿真算例,对该控制算法在轨迹跟踪控制中的应用进行仿真研究。仿真结果表明,该方法可以解决机械系统跟踪控制中的初值不相容问题,同时对约束违约进行了抑制,提高了控制精度及稳定性。

基于功率跟踪式约束的发动机开关控制方法（英文）

针对传统控制方法控制精度低,且在一定程度上容易出现开关实际功率超过发动机可提供功率的情况,提出一种新的基于功率跟踪式约束的发动机开关控制方法。确定发动机开关控制过程中的最大功率。为了便于后续发动机开关控制的分析,介绍了开关支路的功率表达式。构建某支路在其它支路断开情况下的功率跟踪式约束。将功率跟踪式约束转化为适于发动机开关控制的形式。在功率跟踪式约束的条件下,将发动机滚转通道看作一个双积分环节。对发动机启动时间与运行时间进行调整,使其和开关力矩达到一致效果,实现开关控制。在控制量低于设定值的情况下,给出开关控制改进过程。实验结果表明:所提方法控制精度高\收敛性佳,能满足发动机对开关的要求。

约束仅方位跟踪粒子滤波器研究

该文研究了杂波干扰下适用于平面机动目标实时定位的粒子滤波器的设计和实现。运用贝叶斯递推方法,分析了通用粒子滤波算法。应用马尔可夫链理论,建立了目标机动的多模式模型。针对约束仅方位跟踪(CBOT)的实际条件,推导了粒子滤波器中的最优重要密度公式。综合粒子滤波算法和约束仅方位跟踪理论,得出了约束仅方位跟踪粒子滤波器的伪码。对约束仅方位跟踪粒子滤波器的研究表明:在目标受到道路图之类的约束假设下,可以去除对本站作特定机动的要求;可在杂波环境中、目标作任意非线性机动的情况下,实现仅方位跟踪;跟踪定位距离大、角度宽;采用最优重要密度,可大大减少粒子数,降低计算量。

康复步行训练机器人位置和速度跟踪误差同时约束的安全预测控制

康复步行训练机器人通过跟踪医生指定的运动轨迹,帮助患者步行训练,针对运动过程中位置和速度跟踪误差过大,影响康复者的安全问题,提出一种预测控制方法,目的是使康复步行训练机器人从任意位置出发同时实现轨迹和速度跟踪,并将跟踪误差约束在指定范围内,提高系统的安全性。通过离散化康复步行训练机器人的动力学模型,建立了具有控制增量形式的预测模型。在预测时域内,设计轨迹跟踪误差性能优化指标,并构建运动位置和速度跟踪误差约束条件,通过设计辅助运动轨迹并求解控制增量形式的二次规划问题,获得了时域内满足误差约束条件的预测控制。通过仿真和实验研究,结果表明了所提控制方法同时约束位置和速度跟踪误差的有效性和优越性。

直流电机跟踪误差时变约束控制

电机伺服系统中存在各种未建模扰动,引起系统不稳定。在跟踪控制中如何有效的处理扰动并且对直流电机的输出进行限位控制成为目前研究的焦点。针对存在的问题,选取电机位置伺服系统作为研究对象,建立了系统的非线性模型,同时考虑了系统的外干扰等建模不确定性;并且针对未建模干扰等不确定性通过扩张状态观测器进行估计并前馈补偿,提高了实际电机位置伺服系统对外干扰的鲁棒性;而且利用时变非对称障碍Lyapunov函数所设计的非线性鲁棒控制器能够对输出位置跟踪误差进行时变非对称约束,从而对电机的输出进行限位控制。所设计的非线性鲁棒控制器为全状态控制且控制电压连续,更利于在工程实际中应用。仿真结果验证为直流电机跟踪优化控制提供了依据。

考虑人机作用力的康复训练机器人各运动轴最优轨迹跟踪预测控制

针对人机作用力影响康复训练机器人的跟踪性能问题,提出了一种新颖的观测器设计方法,目的是利用系统的位置输出估计人机作用力.同时,为了抑制人机作用力并避免运动中产生较大的跟踪误差而影响康复者安全,设计了非线性控制器,并得到了机器人各运动轴系统模型.进一步,结合预测控制同时约束了系统轨迹跟踪误差和速度跟踪误差,并实现了各运动轴的最优轨迹跟踪.通过仿真结果对比分析和实验研究,表明文中提出人机作用力观测和控制器设计方法的有效性和优越性.

足球场内球员多目标跟踪算法

为解决足球比赛场内球员的多目标跟踪任务中,因场外人员对跟踪的干扰,球员频繁地运动、互相遮挡,以及摄像镜头复杂地移动等情况,造成的跟踪准确度低、跟踪目标ID(identity)保持能力弱的问题,提出一种适用于足球场内球员跟踪的多目标跟踪数据集和多目标跟踪算法。通过条件生成对抗网络分割出球场区域,筛选出球场内的基于YOLOX框架的目标检测结果;在数据关联阶段,设计一种融合IoU(intersection over union)与欧式距离的代价矩阵进行目标间的相似性度量;利用足球比赛上场人数存在上限的先验条件,弹性约束跟踪目标ID的增长。实验结果表明,针对足球场内球员的跟踪问题,该算法能够在多目标跟踪准确度、跟踪目标ID保持能力上有极大提高。

一种有效的圆形小幅员目标检测方法

提出了一种有效的圆形小幅员目标检测方法,首先对原始图像进行一定的预处理,并提取和细化边缘,通过有约束的边缘跟踪方法剔除大量的干扰背景边缘点和噪声,并以分段等间隔点组采样进行圆参数计算,大大减少了参数的无效累积。实验结果表明,该方法对复杂背景下圆形小幅员目标的检测结果可靠性高,实时性好。

改进预设性能的车辆队列滑模控制方法研究

为了增强非线性车辆队列的队列稳定性,并在控制全过程中有效约束跟踪误差,提出一种基于改进预设性能的车辆队列滑模控制方法.设计关于跟踪误差的预设性能函数,通过在函数中为每辆车设置不同的衰减速率,改进前后车的预设性能,从而增强队列稳定性,并确保车辆的跟踪误差始终被约束在允许的范围内.在此基础上,利用径向基神经网络估计车辆的非线性不确定项,设计基于改进预设性能的车辆队列积分滑模控制器,以保证车辆队列的安全稳定行驶.最终通过数值仿真验证所提出控制方法的有效性.研究结果表明:所提出的车辆队列滑模控制方法不再依赖后车信息,知晓前车信息即可增强队列稳定性.

推进器饱和/故障下的无人艇固定时间指定性能容错控制

针对系统动态未知的无人艇(USV)在推进器故障与饱和约束下轨迹跟踪问题,本文提出一种基于固定时间扩张状态观测器的积分滑模容错控制方法.首先,构造固定时间扩张状态观测器,实现未知速度信息、集总未知非线性的准确估计.进而,通过引入一种新型设定时间性能函数,约束位姿跟踪误差,并利用误差转换函数将其转化为无约束误差动态系统.在此基础上,结合固定时间积分滑动模态与饱和补偿动态系统,设计固定时间控制策略,保证系统实际固定时间稳定且位姿跟踪误差严格位于指定范围内.最后,仿真验证所提出控制方法的有效性与优越性.

An Anticipatory Terminal Iterative Learning Control Approach with Applications to Constrained Batch Processes

This work presents an anticipatory terminal iterative learning control scheme for a class of batch proc- esses, where only the final system output is measurable and the control input is constant in each operations. The propgsed approach works well with input constraints provided that the desired control input with respect to the desired trajectory is within the samratiorl bound. The tracking error convergence is established with rigorous mathe- matical analysis. Simulation results .are provided to showthe effectiveness, of the proposed approach.

A novel grasping force control strategy for multi-fingered prosthetic hand

A grasping force control strategy is proposed in order to complete various free manipulations by using anthropomorphic prosthetic hand. The position-based impedance control and force-tracking impedance control are used in free and constraint spaces, respectively. The fuzzy observer is adopted in transition in order to switch control mode. Two control modes use one position-based impedance controller. In order to achieve grasping force track, reference force is added to the impedance controller in the constraint space. Trajectory tracking in free space and torque tracking in constrained space are realized, and reliability of mode switch and stability of system are achieved. An adaptive sliding mode friction compensation method is proposed. This method makes use of terminal sliding mode idea to design sliding mode function, which makes the tracking error converge to zero in finite time and avoids the problem of conventional sliding surface that tracking error cannot converge to zero. Based on the characteristic of the exponential form friction, the sliding mode control law including the estimation of friction parameter is obtained through terminal sliding mode idea, and the online parameter update laws are obtained based on Lyapunov stability theorem. The experiments on the HIT Prosthetic Hand IV are carried out to evaluate the grasping force control strategy, and the experiment results verify the effectiveness of this control strategy.

Adaptive Resource Allocation Algorithm for Internet of Things with Bandwidth Constraint

In order to improve the transmission accuracy and efficiency of sensing and actuating signals in Internet of Things （loT） and ensure the system stability, an adaptive resource allocation algorithm is proposed, which dynami- cally assigns the network bandwidth and priority among components according to their signals＇ frequency domain characteristics. A remote sensed and controlled unmanned ground vehicle （UGV） path tracking test-bed was devel- oped and multiple UGV＇s tracking error signals were measured in the simulation for performance evaluation. Results show that with the same network bandwidth constraints, the proposed algorithm can reduce,, the accumulated and maximum errors of UGV path tracking by over 60% compared with the conventional static algorithm.

Motion constraint Markov network model for multi-target tracking

A typical Markov network for modeling the interaction among targets can handle the error merge problem,but it suffers from the labeling problem due to the blind competition among collaborative trackers. In this paper,we propose a motion constraint Markov network model for multi-target tracking. By augmenting the typical Markov network with an ad hoc Markov chain which carries motion constraint prior,this proposed model can overcome the blind competition and direct the label to the corresponding target even in the case of severe occlusion. In addition,the motion constraint prior is formu-lated as a local potential function and can be easily incorporated in the joint distribution representation of the novel model. Experimental results demonstrate that our model is superior to other methods in solving the error merge and labeling problems simultaneously and efficiently.

Deriving Network-Constrained Trajectories from Sporadic Tracking Points Collected in Location-Based Services

The paper proposes an economical and fast algorithm for deriving trajectories from sporadic tracking points collected in location-based services (LBS). Although many traffic studies or applications can benefit from the derived trajectories, the sporadic tracking points are always implicitly overlooked by most of existing map-matching algorithms. The algorithm proposed in this paper finds network paths or trajectories traveled by vehicles through augmenting GPS data with odometer data. An odometer can provide data of traveled distance which are compared with the lengths of candidate network paths in order to find the most approximate network path approaching the trajectory of a vehicle. Tracking points are classified into anchor points and non-anchor points. The former are used to divide trajectories, and the latter screen candidate network paths. An elliptic selection zone and a reduction process are applied to the selection of possible road segments composing candidate network paths. A brute-force searching algorithm is developed to find candidate network paths and calculate their lengths. A two-step screening process is designed to select the final result from candidate network paths. Finally, a series of experiments are conducted to validate the proposed algorithm.